Update app.py

app.py

@@ -2,27 +2,28 @@ import gradio as gr
 import numpy as np
 import random
 
-# import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
+def load_pipeline(model_id):
+    if torch.cuda.is_available():
+        torch_dtype = torch.float16
+    else:
+        torch_dtype = torch.float32
+
+    pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
+    return pipe.to(device)
+
+# Initialize with default model
+pipe = load_pipeline("CompVis/stable-diffusion-v1-4")
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
+    model_id,
     prompt,
     negative_prompt,
     seed,
@@ -33,6 +34,11 @@ def infer(
     num_inference_steps,
     progress=gr.Progress(track_tqdm=True),
 ):
+    global pipe
+
+    if model_id:
+        pipe = load_pipeline(model_id)
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
@@ -50,7 +56,6 @@ def infer(
 
     return image, seed
 
-
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
@@ -66,7 +71,14 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown(" # Text-to-Image Gradio Template with Model Selection")
+
+        model_id = gr.Text(
+            label="Model ID",
+            show_label=True,
+            placeholder="Enter HuggingFace model ID (e.g., CompVis/stable-diffusion-v1-4)",
+            value="CompVis/stable-diffusion-v1-4",
+        )
 
         with gr.Row():
             prompt = gr.Text(
@@ -86,7 +98,6 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
             )
 
             seed = gr.Slider(
@@ -94,10 +105,10 @@ with gr.Blocks(css=css) as demo:
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=0,
+                value=42,
             )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
 
             with gr.Row():
                 width = gr.Slider(
@@ -105,7 +116,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=512,
                 )
 
                 height = gr.Slider(
@@ -113,31 +124,33 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=512,
                 )
 
             with gr.Row():
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
-                    maximum=10.0,
+                    maximum=20.0,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=7.0,
                 )
 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=50,
+                    maximum=100,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=20,
                 )
 
         gr.Examples(examples=examples, inputs=[prompt])
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
         inputs=[
+            model_id,
             prompt,
             negative_prompt,
             seed,